ArticlePLoS computational biology2022
Non-linear archetypal analysis of single-cell RNA-seq data by deep autoencoders.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- ParTIpy: a scalable framework for archetypal analysis and Pareto task inference.Molecular systems biology · 2026Article
- ArchetypeShift: An R Package Integrating KEGG-Informed Pathway Analysis and IPA-Derived Functional Predictions for Validation of Single-Cell Archetypes.Bioinformatics and biology insights · 2026Article
- Functional archetypes in the human gut microbiome reveal metabolic diversity, stability, and influence disease-associated signatures.Microbiome · 2025Article
- Leveraging autoencoder models and data augmentation to uncover transcriptomic diversity of gingival keratinocytes in single cell analysis.Scientific reports · 2025Article
- MIDAA: deep archetypal analysis for interpretable multi-omic data integration based on biological principles.Genome biology · 2025Article
- Metabolic Objectives and Trade-Offs: Inference and Applications.Metabolites · 2025Review
- Starfysh integrates spatial transcriptomic and histologic data to reveal heterogeneous tumor-immune hubs.Nature biotechnology · 2025Article
- scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles.Genome biology · 2023Article
- eQTL studies: from bulk tissues to single cells.Journal of genetics and genomics = Yi chuan xue bao · 2023Review
- eQTL Studies: from Bulk Tissues to Single Cells.ArXiv · 2023Article
- Computational single cell oncology: state of the art.Frontiers in genetics · 2023Review
- resVAE ensemble: Unsupervised identification of gene sets in multi-modal single-cell sequencing data using deep ensembles.Frontiers in cell and developmental biology · 2023Article
- New Developments and Possibilities in Reanalysis and Reinterpretation of Whole Exome Sequencing Datasets for Unsolved Rare Diseases Using Machine Learning Approaches.International journal of molecular sciences · 2022Review
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Authors and funding
2 authors.
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Abstract
Advances in single-cell RNA sequencing (scRNA-seq) have led to successes in discovering novel cell types and understanding cellular heterogeneity among complex cell populations through cluster analysis. However, cluster analysis is not able to reveal continuous spectrum of states and underlying gene expression programs (GEPs) shared across cell types. We introduce scAAnet, an autoencoder for single-cell non-linear archetypal analysis, to identify GEPs and infer the relative activity of each GEP across cells. We use a count distribution-based loss term to account for the sparsity and overdispersion of the raw count data and add an archetypal constraint to the loss function of scAAnet. We first show that scAAnet outperforms existing methods for archetypal analysis across different metrics through simulations. We then demonstrate the ability of scAAnet to extract biologically meaningful GEPs using publicly available scRNA-seq datasets including a pancreatic islet dataset, a lung idiopathic pulmonary fibrosis dataset and a prefrontal cortex dataset.
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